Join the forefront of the autonomous vehicle revolution with NVIDIA's SWQA team. This critical role involves architecting the security and reliability of next-generation intelligent vehicles. You'll be instrumental in validating software that ensures the safety of millions on the road.

This high-impact position focuses on our industry-leading automotive products. You will influence the core technology stack and build essential tools and frameworks for systems running on Linux and QNX. We seek passionate engineers comfortable with low-level product architecture and complex product use cases.

Design, execute, and automate comprehensive test cases and scenarios for automotive platforms. Identify and track defects through to closure.

Participate in in-depth reviews of product requirements and technical designs, providing feedback for testability and security.

Collaborate with project management, hardware, and software teams to analyze bugs and report findings.

Architect and maintain a distributed test automation framework for high-concurrency workloads.

Develop sophisticated test libraries and automation solutions to enhance development cycles and expand test coverage.

Drive the full automation lifecycle, from analyzing logs and logging defects to leading bug-scrub cycles for quality releases.

A Bachelor's degree in Computer Science, Electronics & Electrical Engineering is required. You should possess 5+ years of experience in automation engineering or software development.

Strong foundational knowledge of QNX or Linux-based operating systems, including system concepts and boot sequences, is essential. Professional experience in System SW validation, encompassing Bootloaders, BSP, ARM Trusted Firmware, Trusted OS (TOS), and Secure Boot protocols, is necessary.

Proficiency in Python or C++ for writing clean, maintainable, and testable system-level code is a must. Deep familiarity with AI-native development tools like Claude Code, Cursor, or LLM APIs is expected. Understanding LLM failure modes and experience building evaluation frameworks for AI-generated outputs are highly valued.